There is a wealth of work on interactive assignment of properties to an image. One approach can be first to segment the image and then associate all pixels in each segment with a different property. For a comprehensive background on state-of-the art interactive segmentation approaches, see [1]. One particularly relevant approach segments images/videos or assigns properties to images/videos by letting the user mark pixels that are within the interior of objects. The following approaches relate to particularly well-known approaches.
Magic Wand [1], allows the user to select a region by marking a point. It may be seen that in graphics programs that employ this technique, such as ArcSoft PhotoStudio® of ArcSoft, Fremont, Calif., USA, selection of a point using the magic wand causes other non-contiguous areas of the picture to be selected. This may be undesirable.
Other known approaches based on scribbles are prone to the same problem. For example, FIG. 1 is a screen shot of an image of which it is required to select only a part using a scribbles-based selection tool such as described in [3] based on colorization, that is, the assignment of colors to a grayscale image. As would be seen more clearly in color image, a single pink scribble assigns a pink hue to the entire image. In practice, however, it may more generally be required to paint only a part of the image, such as the flowers, with the assigned pink color. The method described in [3] is limited in the user experience in that it requires the user to maintain a set of scribbles and delete sometimes a scribble or a part of it.
Bayes matting, Knockout 2 [1] and other multi-scribble approaches [2-11], segment images or assign properties by letting the user mark multiple scribbles. For these methods to provide useful results, the user must mark a plurality of scribbles (also termed “seeds”) that provide at least two different properties. For example, in image matting or segmentation as taught in e.g. [2, 5], the user must provide scribbles for all segments. In colorization as taught in e.g. [3, 4], the user must provide scribbles for a plurality of colors. The workflow of these methods allows the user to build up the plurality of scribbles incrementally by adding or removing a scribble at each iteration. More specifically, these approaches may appear incremental to the user but in fact use the aggregate information provided the totality of the scribbles to compute color assignment. In other words, from the user's point of view, the input provided to the system is the aggregate set of the plurality of scribbles. Therefore, even if the scribbles set is built up incrementally, and even if the user adds a single scribble at each iteration, these methods all employ the sum totality of multiple scribbles in each iteration. Hence the user, in order to control these methods, needs to be aware of the full set of the plurality of scribbles. It would clearly be preferable if the result of each iteration served as the starting point for a subsequent iteration, so that the user could then better gauge how a new scribble would impact on the final result.